The Contingency Effect of Service Employee Personalities on Service Climate
Bibliographic record
Abstract
This study draws on the service climate and Big Five personality literature to examine the association between a service employee’s personality and perception of service climate. The authors further explore the moderating role of employee involvement climate strength on the personality trait–perceived service climate relationship based on situational strength theory. Hypotheses are tested against data collected using a multiple respondent (i.e., employees and customers) method from a national chain of 66 specialty retail stores in fashion and apparel, footwear, accessories, and sports equipment. Findings indicate that an employee who is conscientious, open to experience, and agreeable perceive the service climate to be more positive. Results also suggest that under a strong employee involvement climate, an employee who is conscientious, emotionally stable, and agreeable has a perception of service climate that is less positive. Finally, employees' perception of service climate was positively related to customers' satisfaction with decision to visit the store. Findings have practical implications for hiring and promoting employees with certain personalities that are more conducive to forming a positive perception of service climate. Further, results suggest that when retail stores have a weak employee involvement climate, stores benefit from possessing employees that are conscientious, emotionally stable, and agreeable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".